Using matrix frame to present road traffic injury pattern

O R I G I N A L C O N T R I B U T I O N

Open Access

Using matrix frame to present road traffic

injury pattern

Chien-Hsing Wang

, Wan-Hua Hsieh

, Fu-Wen Liang

and Tsung-Hsueh Lu

Abstract

Background:Although many epidemiological studies have presented road traffic injuries (RTIs) according to the victim’s mode of transport, very few have mentioned the mode of transport of the victim’s counterparts. We sought to use matrix frame to present the pattern of RTIs based on the International Classification of Diseases, Tenth Revision (ICD-10) codes.

Methods:Patients admitted to Hualien Tzu Chi Hospital, Taiwan, for RTIs from January 1, 2013 to December 31, 2016 were included. The numbers and proportions of various crash types of RTIs were presented using a matrix frame. The row margin of the matrix is the second character of ICD-10 codes V00–V79 (victim’s mode of transport), and the column margin of the matrix is the third character of ICD-10 codes V00–V79 (mode of transport of victim’s counterpart), constituting a 80-cell grid.

Results:In total, 2727 patients were included. The cell with the highest proportion in the matrix grid was ICD-10 code V23“motorcycle rider injured in collision with car, pick-up truck or van”(27.0%, 737/2727), followed by that of V27“motorcycle rider injured in collision with fixed or stationary object”(12.5%, 342/2727) and V28“motorcycle rider injured in noncollision transport accident”(12.2%, 334/2727). The matrix pattern of RTIs differed with sex and age.

Conclusions:By using the matrix frame, we can easily understand the RTI pattern for different demographic groups and identify the priority crash types.

Keywords:Transport accidents, Road traffic injuries, Pattern of injury, Epidemiology

Background

Although many epidemiological studies have presented road traffic injuries (RTIs) according to the victim’s mode of transport (e.g., pedestrian, bicycle, two-wheel motor-cycle, and car) (Chokotho et al.2013; Majdan et al.2015; Spoerri et al. 2011; Watson et al. 2015), very few have mentioned the mode of transport of the victim’s counter-parts, which could provide a more complete picture of the crash event, thus facilitating the design of relevant inter-vention programs. For instance, in the Netherlands, bicy-clists injured in crashes not involving motor vehicles had a higher number of serious injuries than bicyclists injured in crashes involving motor vehicles; in addition, they had

different implications for prevention measures, such as the design of bicycle tracks, mobility advice for older bicyclists, and campaigns to encourage bicycle light use (Weijermars et al.2016).

Compared with the International Classification of Diseases (ICD), Ninth Revision (ICD-9) codes, an innova-tive feature of the ICD, Tenth Revision (ICD-10) codes for RTI is the use of the mode of transport modular coding frame (Langley and Chalmers1999), which can be arrayed as a matrix (National Center for Health Statistics 2013). The row margin of the matrix is the second character of ICD-10 codes V00–V79 (victim’s mode of transport), and the column margin of the matrix is the third character of ICD-10 codes V00–V79 (the mode of transport of victim’s counterpart), constituting a 80-cell grid (Table1). However, no study thus far has presented the RTI pattern by using a matrix frame. In this study, we sought to use matrix frame * Correspondence:[email protected]

5

NCKU Research Center for Health Data and Department of Public Health, National Cheng Kung University, Tainan, Taiwan

6_{Department of Public Health, College of Medicine, National Cheng Kung}

University, No. 1, Dah Hsueh Road, Tainan 701, Taiwan Full list of author information is available at the end of the article

to present the RTI pattern among people admitted to one medical center in Eastern Taiwan affected by RTI.

Methods

We included patients admitted to Hualien Tzu Chi Hospital, Taiwan, for RTIs from January 1, 2013 to December 31, 2016 and extracted their demographic data (age and sex), ICD-10 codes of external causes of RTIs (V00–V79). The numbers and proportions of vari-ous crash types of RTIs were presented using a matrix frame. The users of the matrix can identify the victim’s mode of transport (e.g., ICD-10 code V2 motorcycle rider) in row margin first and then the mode of trans-port of counterpart (e.g., ICD-10 code VX3 car) in column margin and get the number and proportion of cases. To properly interpret of the comparisons of pro-portions between different crash types, we calculated 95% confidence intervals for each proportion.

We used the user-friendly self-service business intelligence software Tableau to create a dashboard; therefore, we could select the dimension (specific age group or sex) of our choice. To more clearly visualize the crash types occurring in high proportions, we used a highlighted table: the darker the cell color, the higher the percentage of a particular crash type was. The num-ber and 95% confidence interval of each proportion are

displayed in tooltips that pop out when the user hovers over the mark.

Results

A total of 2727 patients were included in the analysis. According to the row margin of the matrix (victim’s mode of transport: second character of ICD-10 codes

V00–V79), the ICD-10 code V2 “motorcycle rider”

accounted for the highest proportion (70%, 1901/2727; Fig. 1); furthermore, the proportions of V2 for victims aged 0–14, 15–24, 25–44, 45–64, and > = 65 years were 35%, 85%, 71%, 66%, and 65%, respectively.

In the column margin of the matrix (the mode of transport of victim’s counterpart: third character of ICD-10 codes V00–V79), ICD-ICD-10 code VX3 “car, pick-up truck or van” accounted for the highest proportion (36%, 906/2516); moreover, the proportions of VX3 for victims aged 0–14, 15–24, 25–44, 45–64, and > = 65 years were 38%, 41%, 32%, 34%, and 38%, respectively.

Of the 80 cells in the matrix grid, the cell with highest proportion (darkest color) in the matrix was that of V23 “motorcycle rider injured in collision with car, pick-up truck or van”(27.0%, 737/2727; Fig.1), followed by that of V27 “motorcycle rider injured in collision with fixed or stationary object”(12.5%, 342/2727) and V28 “motor-cycle rider injured in noncollision transport accident”

(12.2%, 334/2727). The proportion of V23 for female pa-tients was 29.5% (359/1216), which is higher than that for male patients (25.0%, 378/1511).

For patients aged 15–24 years, the cell with the highest percentage of RTIs was that of V23 (36.3%, 213/586), followed by that of V27 (16.6%, 97/586) and V28 (12.6%, 74/586) (Fig. 2). However, for patients aged 0–14 years, the cell with the highest percentage of RTIs was that of V03 “pedestrian injured in colli-sion with car, pick-up truck or van” (14.6%, 14/96), followed by that of V23 (11.5%, 11/96) and V28 (10.4%, 10/96) (Fig.3).

Discussion

The findings of this study demonstrate that the main mode of transport of RTI victims in Taiwan was the motorcycle, accounting for seven-tenths of all RTIs. By contrast, in the Netherlands, bicycles accounted for three-fifths of all RTIs in 2011 (Weijermars et al.2016). Therefore, the RTI patterns may differ considerably among countries.

Regarding the mode of transport of the victim’s coun-terpart, the number of injured cyclists in crashes not in-volving motor vehicles was 5 times the number of injured cyclists in crashes involving motor vehicles in the Netherlands (Weijermars et al. 2016). However, ac-cording to our findings, in Taiwan, the number of in-jured bicyclists in crashes involving motor vehicles (ICD-10 codes V23, V24, and V25) was only 1.5 times the number of injured bicyclists in crashes not involving

motor vehicles (ICD-10 codes V20, V21, V26, V27, and V28).

Despite the differences in the RTI pattern between Taiwan and the Netherlands, the proportion of RTIs in vulnerable road users was similar between countries: 88% in Taiwan and 86% in the Netherlands. The term “vulnerable road user”refers to people at the highest risk in traffic; these people are not protected by an outside shield, such as pedestrians, bicyclists, and mo-torcyclists, and have few or no external protective de-vices to absorb energy in a collision, making them the weak counterpart in a road traffic crash (Costant and Lagarde 2010). Several measures for preventing RTIs among vulnerable road users (e.g., helmet use, conspicuity aids, and avoiding alcohol use) could be applied to motorcyclists in Taiwan and bicyclists in the Netherlands.

Different matrix frame formats have been proposed for presenting injury-related statistics. The most well-known is the Barell matrix, which details affected body region and nature of injury (e.g., fracture) (Barell et al. 2002; Fingerhut and Warner 2006). An-other matrix is the external cause of injury mortality matrix, which details RTIs by the mechanism and in-tent of injury (McLoughlin et al. 1997; Fingerhut and McLoughlin 2001; Fingerhut 2004; Minino et al. 2006). By using the external cause of injury mortality matrix, we determined that the decrease in the mor-tality trends of some unintentional injuries might be due to the increase in mortality trends of the same

mechanism of injury with an undetermined intent (Lu 2002). However, no study thus far has used the mode of transport matrix frame to present the RTI pattern.

This study has two strengths: (1) the use of the mode of transport matrix frame to present the pattern of RTIs and (2) the use of a visualization dashboard to select the demographic group of choice.

However, this study also has several limitations. First, the mode of transport matrix frame is constructed on the basis of the ICD-10 codes; therefore, if the medical record documentation of the mode of transport is not as specific as required, many RTIs will be classified as “un-specified”; thus, no useful information can be obtained. Nevertheless, the quality of health record documentation on the mode of transport at Hualien Tzu Chi Hospital is relatively high: only 11% of cases have been classified as ICD-10 code VX9 “other and unspecified”. Second, the information of only two dimensions (second and third characters of ICD-10 codes V00–V79) could be pre-sented in the matrix. The information of the fourth character of ICD-10 codes V00–V79 regarding whether the motor vehicle occupant was the driver or passen-ger and whether it was a traffic or nontraffic accident could not be presented in the same matrix. A solu-tion to this limitasolu-tion is the use of the drill-down function in the visualization dashboard. Third, be-cause RTIs may be concentrated under particular crash types, the number of RTIs in many cells of the matrix remains zero. In other words, the presentation

of the RTI pattern by using a matrix frame may oc-cupy a larger space than that occupied by the trad-itional presentation method.

Conclusion

In conclusion, by presenting the mode of transport of both the victim and the victim’s counterpart in a matrix frame, we could easily understand the RTI pattern and identify the priority crash types. Studies using matrix frames to compare RTI patterns between countries with different modes of transport are warranted.

Acknowledgements

The authors thank Ms. Pai-Huan Lin for data visualization.

Authors_’contributions

CW collected and analyzed the data, reviewed the literature and draft the manuscript. WH helped analyzed the data and critical review the manuscript. FL provided crucial statistical suggestions, completed data analysis, and revising the manuscript critically. TL conceived the study, supervise the literature review and analysis and critical review the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

This study was approved by the Institutional Review Boards of Chi-Mei

Medical Center (10406_–003) and TzuChi Hospital (104_–67-B).

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Author details

1_{Division of Plastic Surgery, Department of Surgery and Trauma Center,}

Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan.2School of Medicine, Tzu Chi University, Hualien, Taiwan.

3_{Department of Public Health, Tzu Chi University, Hualien, Taiwan.}

4_{Department of Public Health, College of Health Sciences, Kaohsiung Medical}

University, Kaohsiung, Taiwan.5_{NCKU Research Center for Health Data and}

Department of Public Health, National Cheng Kung University, Tainan, Taiwan.6_{Department of Public Health, College of Medicine, National Cheng}

Kung University, No. 1, Dah Hsueh Road, Tainan 701, Taiwan.

Received: 14 December 2017 Accepted: 5 April 2018

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